Information provision device for use in vehicle

ABSTRACT

A first driving instability determining unit estimates driving instability based on a difference value between plural traveling state distributions of different time ranges on the basis of the traveling state data. A second driving instability determining unit estimates driving instability by a process different from the process used in the first driving instability determining unit. A learning completion determining unit determines that the learning is completed when a predetermined learning time elapses from the start of collection of the traveling state data, depending on a degree of learning at which the traveling state distribution calculated by a first traveling state distribution calculating unit is matched with the driving characteristic of a driver. An instability selecting unit selects the instability estimated by the first driving instability determining unit when the learning is completed and selects the instability estimated by the second driving instability determining unit when the learning is not completed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional application of U.S. application Ser.No. 14/112,456, filed Oct. 17, 2013, which is the National StageApplication of International Application No. PCT/JP2012/002091, filedMar. 26, 2012, which claims priority to Japanese Application No.2011-094343, filed Apr. 20, 2011, the entire contents of all areincorporated herein by reference.

TECHNICAL FIELD

The present invention relates to a technique of presenting an unstabledriving state to a driver.

BACKGROUND ART

In a driving support apparatus for a vehicle described in PatentDocument 1, a long-time traveling state distribution corresponding tonormal driving characteristics and a short-time traveling statedistribution corresponding to current driving characteristics arecalculated and an unstable driving state is determined on the basis ofthe magnitude of a difference between the calculated two distributions.It is stated that it is possible to accurately detect an unstable stateregardless of a variation in traffic environment according to thismethod.

PRIOR ART DOCUMENT Patent Document

Patent Document 1: JP 2009-9495 A

SUMMARY OF THE INVENTION Problem to be Solved

However, in the technique disclosed in Patent Document 1, when learningfor acquiring a long-time traveling state distribution is not completedto such an extent that the normal driving characteristics are regardedas being understood, i.e., when the normal driving characteristics arenot understood, the detection accuracy of a driver's unstable drivingstate is lowered.

The present invention is made in view of the above-mentionedcircumstances and an object thereof is to present an unstable drivingstate to a driver even when learning of normal driving characteristicsis not completed.

Solution to the Problem

In order to achieve the above-mentioned object, according to an aspectof the present invention, there is provided a first driving instabilitydetermining unit for estimating driving instability based on adifference between plural traveling state distributions of differenttime ranges on the basis of acquired traveling state data. According toan aspect of the present invention, there is provided a second drivinginstability determining unit for estimating the driving instability onthe basis of the traveling state data through the use of a processdifferent from the estimation process of the first driving instabilitydetermining unit. According to an aspect of the present invention, theinstability estimated by the first driving instability determining unitis selected when a predetermined learning time elapses from the start ofcollection of the traveling state data and it is determined that thelearning is completed, and the instability estimated by the seconddriving instability determining unit when it is determined that thelearning is not completed. According to an aspect of the presentinvention, instability information based on the selected instability ispresented to a driver.

Advantageous Effects of the Invention

According to an aspect of the present invention, it is possible topresent an unstable driving state to a driver depending on the drivinginstability estimated by the second driving instability determining uniteven when the learning for acquiring the traveling state distributionsused by the first driving instability determining unit is not completedto such an extent that the normal driving characteristics are regardedas being understood.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a configuration of a vehicle accordingto embodiments of the present invention;

FIG. 2 is a diagram illustrating an example of a system configurationaccording to a first embodiment to a fourth embodiment of the presentinvention;

FIG. 3 is a diagram illustrating a process in an information providingunit according to the first embodiment of the present invention;

FIGS. 4A and 4B are diagrams+ illustrating an example of informationpresented to a driver;

FIG. 5 is a diagram illustrating an example of calculating relativeentropy;

FIG. 6 is a diagram illustrating signs used to calculate relativeentropy;

FIG. 7 is a diagram illustrating a method of calculating a past orlong-time distribution and an immediately-previous distribution based onsteering angle prediction error data;

FIG. 8 is a diagram illustrating a method of calculating relativeentropy;

FIG. 9 is a diagram illustrating sections of a steering angle predictionerror;

FIG. 10 is a diagram illustrating a process in an information providingunit according to a second embodiment of the present invention;

FIG. 11 is a diagram illustrating a process in an information providingunit according to a third embodiment of the present invention;

FIG. 12 is a diagram illustrating a process in an information providingunit according to a fourth embodiment of the present invention;

FIG. 13 is a diagram illustrating an example of a system configurationaccording to a fifth embodiment of the present invention;

FIG. 14 is a diagram illustrating a process in an information providingunit according to the fifth embodiment of the present invention;

FIG. 15 is a diagram illustrating an example of a system configurationaccording to a sixth embodiment of the present invention; and

FIG. 16 is a diagram illustrating a process in an information providingunit according to the sixth embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS First Embodiment

First, a first embodiment of the present invention will be describedwith reference to the accompanying drawings.

(Configuration)

FIG. 1 is a diagram illustrating a configuration of a vehicle having aninformation provision device for use in vehicle according to thisembodiment mounted thereon.

As shown in FIG. 1, a vehicle of this embodiment includes an acceleratorpedal opening degree sensor 1, a brake pedal operation amount sensor 2,a steering angle sensor 3, a vehicle velocity sensor 4, a blinkerdetecting sensor 5, a meter display 6, a navigation system 7, a G sensor8, a vehicle ahead detecting device 9, and a controller 100. The vehicleto which the present invention is applied does not have to include theabove-mentioned sensors and other overall equipment. The sensors used inother embodiments are described together.

The accelerator pedal opening degree sensor 1 detects an opening degree(instructed acceleration value) of an accelerator pedal as an instructedacceleration value. The detected opening degree is output to thecontroller 100.

The brake pedal operation amount sensor 2 detects an operation amount(instructed braking value) of a brake pedal as an instructed brakingvalue. The detected operation amount is output to the controller 100.

The steering angle sensor 3 is, for example, an angle sensor attached tothe vicinity of a steering column or a steering wheel (not shown) anddetects a steering angle subjected to a driver's steering operationbased on rotation of a steering shaft. The detected steering angle isoutput to the controller 100.

The vehicle velocity sensor 4 detects a vehicle velocity, for example,by detecting the number of revolutions of a vehicle wheel. The detectedvehicle velocity is output to the controller 100. The vehicle velocitysensor 4 may detect the vehicle velocity on the basis of a signal to themeter display 6.

The blinker detecting sensor 5 detects a blinker state of a blinkerlever. The detected blinker state is output to the controller 100.

The information presentation device outputs an alarm or otherpresentations as a sound or an image in response to a control signalfrom the controller 100. The information presentation device includes aspeaker 10 that provides information to a driver, for example, using abuzzer sound or a voice, and a display unit that provides informationthrough a display of an image or texts. A display monitor of thenavigation system 7 may be used in common as the display unit.

The navigation system 7 includes a GPS receiver, a map database, and adisplay monitor and is a system that performs route search, routeguidance, and the like. The navigation system 7 is capable of acquiringinformation on such as a type of a road on which the vehicle travels ora width of the road on the basis of the current position of the vehicleacquired from the GPS receiver and road information stored in the mapdatabase.

The G sensor 8 detects a longitudinal acceleration or a transverseacceleration generated in the vehicle. The detected acceleration isoutput to the controller 100.

The vehicle ahead detecting device 9 detects other vehicles and otherobjects present on the front side in the traveling direction of thevehicle. In this embodiment, the distance to an object is detected. Thevehicle ahead detecting device 9 includes, for example, a laser distancemeter. The detected distance is output to the controller 100 asinformation for calculating an inter-vehicle distance, an inter-vehicletime, a relative velocity, and the like.

The controller 100 is an electronic control unit including a CPU and CPUperipheral components such as a ROM and a RAM, and includes aninformation providing unit 100A that performs an information provisioncontrol process. The information providing unit 100A of the controller100 analyzes driving characteristics of a driver on the basis of thesignals detected by the accelerator pedal opening degree sensor 1, thebrake pedal operation amount sensor 2, the steering angle sensor 3, andthe like and determines a degree of driving instability such as adisorder of a driver's driving operation. The information providing unit100A presents an alarm or other information to the driver depending onthe degree of driving instability to attract the driver's attention.

FIG. 2 is a diagram illustrating an example of a system configuration ofan information provision device for use in a vehicle including theinformation providing unit 100A according to this embodiment.

The information provision device for use in a vehicle according to thisembodiment uses information from the steering angle sensor 3 astraveling state data, as shown in FIG. 2. A visual informationpresenting device and an auditory information presenting device areexemplified as an information presenting device. The visual informationpresenting device is, for example, the meter display 6 or the displayunit of the navigation system 7. The auditory information presentingdevice is, for example, the speaker 10.

The timer 50 is used to acquire a traveling time from the start ofcollection of the traveling state data.

The same configuration as in the system configuration shown in FIG. 2 isemployed by systems according to a second embodiment to a fourthembodiment to be described later.

The process of the information providing unit 100A will be describedwith reference to FIG. 3. The process of the information providing unit100A is performed in a predetermined control cycle (for example, 100msec).

First, in step S1010, the information providing unit 100A acquires thefollowing data as vehicle information data. That is, the informationproviding unit 100A acquires a steering angle as the traveling statedata from the steering angle sensor 3.

In step S1030, the information providing unit 100A determines a learningsituation. In this embodiment, the traveling time from the start ofcollection of data is used to determine the learning situation. A degreeof learning SD may be calculated using the number of data piecescollected.

Specifically, in step S1030, the information providing unit 100Acalculates the degree of learning SD on the basis of the followingexpression.

Degree of learning SD=traveling time (s)/(time range×coefficient)

Traveling time: time after travelingTime range: time range (for example, 2000 seconds) of a traveling statedistributionCoefficient: coefficient (for example, 5) associated with a convergencetime

The value of (time range×coefficient) corresponds to a predeterminedlearning time.

The traveling time is acquired from the timer 50.

In step SI030, the information providing unit 100A then determines alearning situation from the calculated degree of learning SD.

In this embodiment, when the degree of learning SD is equal to or morethan “1”, it is determined that the learning situation is alearning-completed situation. On the other hand, when the degree oflearning SD is less than “1”, it is determined that the learningsituation is a learning-uncompleted situation.

In step S1040, the information providing unit 100A determines aninstability calculating method on the basis of the learning situationdetermined in step S1030. Specifically, the information providing unit100A performs the process of step S1050 when it is determined that thelearning situation is a learning-completed situation (the degree oflearning SD≧1). On the other hand, the information providing unit 100Aperforms the process of step S1070 when it is determined that thelearning situation is a learning-uncompleted situation (the degree oflearning SD<1).

When it is determined that the learning is completed and the processprogresses to step S1050, the information providing unit 100A calculatesplural driving traveling state distributions using a steering entropymethod and calculates a difference value (relative entropy) between thedistributions. Thereafter, the process progresses to step S1060.

Specifically, in step S1050, the information providing unit 100Acalculates the difference value for determining how the driver's currentdriving operation is different from the normal driving operation, i.e.,whether the current driving operation is unstable in comparison with thenormal driving operation, on the basis of the steering angle when thedriver performs a steering operation. That is, in step S1050, relativeentropy (feature amount, instability) is calculated as a valueindicating a disorder that is an unsmooth driving operation. In general,in a state where a driver does not pay attention to the drivingoperation, the time in which the steering is not performed is longerthan that of the normal driving operation in which the driver paysattention to the driving, and thus a large steering angle error isaccumulated. Therefore, the corrected steering amount when the driverpays attention to the driving again increases. In this embodiment, therelative entropy RHp is calculated using this characteristic.Specifically, a steering error distribution (traveling statedistribution) accumulated in the past or for a long time previous to thecurrent and a driver's steering error distribution (traveling statedistribution) in the current time acquired for a short time, i.e.,plural traveling state distributions of different time ranges, arecalculated. With the steering error distribution for a long time whichis considered as the normal driving characteristics as a comparisonreference, the relative entropy RHp is calculated based on the long-timesteering error distribution and the current short-time steering errordistribution.

Here, the relative entropy RHp is a physical quantity indicating adifference value (distance) between the two steering error distributionsand represents the degree of difference between the two steering errordistributions, i.e., by what the two steering error distributions departfrom each other. The stability of the current immediately-previoustraveling state relative to the past long-time traveling state (normaldriving characteristics) can be evaluated using the calculated value ofrelative entropy.

An example of calculating the steering error distribution accumulatedfor a long time, the driver's current steering error distributionacquired for a short time, and the difference value (relative entropy)between the distributions will be described later.

In step S1060, the information providing unit 100A determines anunstable driving state on the basis of the difference value.

In step S1060 of this embodiment, the difference value calculated instep S1050 is compared with a predetermined threshold value fordetermination. When the difference value is larger than the thresholdvalue for determination, it is determined that the driving state isunstable. Thereafter, the process progresses to step S1100.

On the other hand, when it is determined in step S1040 that the learningis not completed, the process progresses to step S1070.

In step S1070, the information providing unit 100A calculates a currentfeature amount (absolute entropy) using the current steering errordistribution of the shorter time range. Thereafter, the processprogresses to step S1080. The absolute entropy is an expected valueappearing in the subject traveling state distribution.

In step S1080, the information providing unit 100A reads the pastdriving feature amount. The past driving feature amount is the finalvalue (absolute entropy) at the time of traveling in the past.Thereafter, the process progresses to step S1090.

In step S1090, the information providing unit 100A compares the currentfeature amount Hp_current calculated in step S1070 with the referencefeature amount obtained by multiplying the past driving feature amountHp_old read in step S1080 by a coefficient k, as expressed in thefollowing expression. When the current feature amount Hp_current islarger than the reference feature amount (Hp_old*k), it is determinedthat the traveling state is unstable. Thereafter, the process progressesto step S1100.

Hp_current/(Hp_old*k)>1

Here, the coefficient k is set to, for example, 1.5.

In step S1100, the information providing unit 100A performs aninformation presenting process when it is determined in step S1060 orstep S1090 that the traveling state is unstable.

An example of information to be presented is shown in FIGS. 4A and 4B.That is, when the degree of learning SD is equal to or more than “1” andit is determined that the traveling state is unstable, the informationpresenting device displays a warning as shown in FIG. 4A and presents awarning voice such as “Driving is disturbed. Please drive withattention!”.

On the other hand, when the degree of learning SD is less than “1” andit is determined that the traveling state is unstable, the estimationaccuracy may be low and thus the information presenting device presentsa warning voice in a kind of gentle expression such as “How are yourcondition? Please, continuously drive safely!”.

In this way, the information to be provided is changed depending on thedegree of learning SD.

In step S1110, the current feature amount (absolute entropy) is stored.The current feature amount (the feature amount calculated in step S1070)is stored for comparison in the next trip (traveling).

Thereafter, the process is terminated and returns.

An example of a process of calculating the steering error distribution(traveling state distribution) accumulated for a long time, the driver'scurrent steering error distribution (traveling state distribution)acquired for a short time, and the difference value (relative entropy)between the distributions will be described with reference to FIG. 5.

Details of this process are continuously performed at a constantinterval, for example, every 50 msec.

In step S10, a traveling scene of the vehicle is estimated (detected) todetermine whether the traveling scene is a traveling scene in which therelative entropy RHp is calculable. Here, when a vehicle velocity V lieswithin a predetermined vehicle velocity range (for example, 40 km/h to120 km/h), it is determined that the traveling scene is a travelingscene in which the relative entropy RHp is calculable. That is, a casewhere the vehicle velocity is extremely slow and a case where thevehicle velocity is extremely fast are excluded from the calculabletraveling scene so as to effectively calculate the relative entropy RHpusing a steering angle signal.

In step S20, it is determined whether the current vehicle velocity Vdetected by the vehicle velocity sensor 4 lies within a predeterminedvehicle velocity range. When it is determined that the vehicle velocityV lies within the predetermined vehicle velocity range and the travelingscene is a traveling scene in which the relative entropy RHp iscalculable, the process progresses to step S30 so as to calculate therelative entropy RHp. On the other hand, when it is determined that thevehicle velocity V does not lie within the predetermined range, theprocess is terminated.

In step S30, a current steering angle signal θ detected by the steeringangle sensor is read as a driver's driving operation amount to bedetected to detect the driver's unstable driving state. In step S31, asteering angle prediction error θe is calculated based on the read valueof the steering angle signal θ.

Here, special signs and names thereof used to calculate the relativeentropy RHp are shown in FIG. 6. A smoothed steering angle valueθn-tilde is a steering angle from which the influence of quantizationnoise is reduced. An estimated steering angle value θn-hat is a valueobtained by estimating the steering angle at the time of sampling on theassumption that the steering is smoothly carried out. As expressed byExpression 1, the estimated steering angle value θn-hat is acquired byperforming a second-order Taylor expansion process on the smoothedsteering angle value θn-tilde.

$\begin{matrix}{``{{Math}\mspace{14mu} 1}"} & \; \\{{\hat{\theta}}_{n} = {{\overset{\sim}{\theta}}_{n - 1} + {\left( {t_{n} - t_{n - 1}} \right)\left( \frac{{\overset{\sim}{\theta}}_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}}{t_{n - 1} - t_{n - 2}} \right)} + {\frac{\left( {t_{n} - t_{n - 1}} \right)}{2}\left( {\frac{\theta_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}}{t_{n - 1} - t_{n - 2}} - \frac{{\overset{\sim}{\theta}}_{n - 2} - {\overset{\sim}{\theta}}_{n - 3}}{t_{n - 2} - t_{n - 3}}} \right)}}} & \left( {{Expression}\mspace{14mu} 1} \right)\end{matrix}$

In Expression 1, to represents the sampling time of the steering angleθn.

The smoothed steering angle value θn-tilde is calculated as an averagevalue of three neighboring steering angles θn in accordance withExpression 2 so as to reduce the influence of quantization noise.

$\begin{matrix}{``{{Math}\mspace{14mu} 2}"} & \; \\{{\overset{\sim}{\theta}}_{n - k} = {\frac{1}{3}{\sum\limits_{i = {- l}}^{l}\; \theta_{n - {kl} + 1}}}} & \left( {{Expression}\mspace{14mu} 2} \right)\end{matrix}$

In Expression 2, l represents the number of samples of the steeringangles θn included in 150 msec when the calculation time interval of thesmoothed steering angle value θn-tilde is set to 150 msec, i.e., theminimum time interval which a human being can intermittently manipulatein a manual operation.

When the sampling interval of the steering angle θn is defined as Ts,the number of samples l is expressed by Expression 3.

l=round(0.15/Ts)  (Expression 3)

In Expression 2, k has values of 1, 2, and 3, and the smoothed valueθn-tilde can be calculated using (k*l) on the basis of three steeringangles θn as sum of the steering angles at the intervals of 150 msec andneighboring steering angles adjacent thereto. Therefore, the estimatedvalue θn-hat calculated on the basis of the smoothed value θn-tilde issubstantially calculated based on the steering angle θ obtained at theintervals of 150 msec.

The steering angle prediction error θe at the time of sampling can becalculated by Expression 4 as a difference between the estimatedsteering angle value θn-hat when it is assumed that the steeringoperation is smoothly carried out and an actual steering angle value θn.

“Math 3”

θe=θn−{circumflex over (θ)}n   (Expression 4)

Here, the steering angle prediction error θe is calculated for thesteering angle θn every the minimum time interval, i.e., 150 msec, whicha human being can intermittently manipulate.

A specific method of calculating the steering angle prediction error θewill be described below. The sampling interval Ts of the steering anglesignal θ is set to, for example, 50 msec. First, three smoothed steeringangle values θn-tilde are calculated in accordance with Expression 2using three neighboring steering angles θn with an interval of 150 msec.The three smoothed steering angle values θn-tilde are expressed byExpression 5.

$\begin{matrix}{``{{Math}\mspace{14mu} 4}"} & \; \\{{{\overset{\sim}{\theta}}_{n - 1} = {\frac{1}{3}\left( {\theta_{n - 4} + \theta_{n - 3} + \theta_{n - 2}} \right)}},{{\overset{\sim}{\theta}}_{n - 2} = {\frac{1}{3}\left( {\theta_{n - 7} + \theta_{n - 6} + \theta_{n - 5}} \right)}},{{\overset{\sim}{\theta}}_{n - 3} = {\frac{1}{3}\left( {\theta_{n - 10} + \theta_{n - 9} + \theta_{n - 8}} \right)}},} & \left( {{Expression}\mspace{14mu} 5} \right)\end{matrix}$

The estimated steering angle values θn-hat are calculated in accordancewith Expression 1 using the calculated three smoothed steering anglevalues θn-tilde. The estimated values θn-hat are expressed by Expression6.

$\begin{matrix}{``{{Math}\mspace{14mu} 5}"} & \; \\\begin{matrix}{{\hat{\theta}}_{n} = {{\overset{\sim}{\theta}}_{n - 1} + {{Ts} \cdot \frac{{\overset{\sim}{\theta}}_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}}{Ts}} +}} \\{{\frac{Ts}{2}\left( {\frac{{\overset{\sim}{\theta}}_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}}{Ts} - \frac{{\overset{\sim}{\theta}}_{n - 2} - {\overset{\sim}{\theta}}_{n - 3}}{Ts}} \right)}} \\{= {{\overset{\sim}{\theta}}_{n - 1} + \left( {{\overset{\sim}{\theta}}_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}} \right) + {\frac{1}{2}\left\lbrack {\left( {{\overset{\sim}{\theta}}_{n - 1} - {\overset{\sim}{\theta}}_{n - 2}} \right) - \left( {{\overset{\sim}{\theta}}_{n - 2} - {\overset{\sim}{\theta}}_{n - 3}} \right)} \right\rbrack}}}\end{matrix} & \left( {{Expression}\mspace{14mu} 6} \right)\end{matrix}$

The steering prediction error θe is calculated in accordance withExpression 4 using the calculated estimated steering angle values θn-hatand the actual steering angle θn.

In step S40, data of the steering angle prediction error θe for apredetermined time of T seconds which is calculated up to now and storedin the memory of the controller 100 is updated by adding the currentvalue of the steering angle prediction error θe calculated in step S31thereto. That is, the earliest data before T seconds out of theaccumulated data of the steering angle prediction error θe is deletedand the current value calculated in step S31 is input instead as thelatest data of the steering angle prediction error θe. Accordingly, thedata of the steering angle prediction error θe before T seconds from thecurrent value is accumulated. The predetermined time T is set to, forexample, T=3600 seconds (=1 hour) so as to accumulate long-period datasufficient to calculate a long-time error distribution which is acomparison reference for determining the unstable state of the currentdriving operation.

In step S50, past or long-time steering angle prediction errordistribution 1 is calculated which serves as the comparison reference ofthe steering angle prediction error distribution. Here, as shown in FIG.7, the past steering angle prediction error distribution is calculated,for example, using data of 180 seconds based on the data before Tseconds. Specifically, the accumulated past steering angle predictionerror θe is classified into nine prediction error sections b1 to b9 andthe probability pi (=p1 to p9) of the frequency of the steering angleprediction error θe included in each section bi with respect to thetotal frequency is calculated. The calculated past distribution is usedas the comparison reference of the steering angle prediction errordistribution. The range of the prediction error section bi is set inadvance so as to be constant in all the sections b1 to b9.

When the long-time steering angle prediction error distribution iscalculated, all data of 3600 seconds from before T seconds to thecurrent time are used. Specifically, the accumulated long-time steeringangle prediction error θe is classified into nine prediction errorsections b1 to b9 and the probability pi (=p1 to p9) of the frequency ofthe steering angle prediction error θe included in each section bi withrespect to the total frequency is calculated. The calculated pastdistribution (or long-time distribution) is used as a Past (orlong-time) steering angle prediction error distribution 1 serving as thecomparison reference.

In step S51, a current steering angle prediction error distribution 2 iscalculated. Here, as shown in FIG. 7, the current steering angleprediction error distribution 2 is calculated using immediately-previousdata of 180 seconds from the current time. Specifically, the data of thesteering angle prediction error θe of immediately-previous 180 secondsis classified into nine prediction error sections b1 to b9 and theprobability qi (=q1 to q9) of the frequency of the steering angleprediction error θe included in each section bi with respect to thetotal frequency is calculated.

In step S70, the relative entropy RHp is calculated using past (orlong-time) steering angle prediction error distribution 1 and currentsteering angle prediction error distribution 2. As shown in FIG. 8, therelative entropy RHp is a difference value (distance) between currentsteering angle prediction error distribution 2 and past (or long-time)steering angle prediction error distribution 1 as the comparisonreference. The relative entropy RHp can be calculated using Expression7.

$\begin{matrix}{``{{Math}\mspace{14mu} 6}"} & \; \\{{RHp} = {\sum\limits_{\;}\; {{q_{i} \cdot \log_{9}}\frac{q_{j}}{p_{i}}}}} & \left( {{Expression}\mspace{14mu} 7} \right)\end{matrix}$

The relative entropy RHp becomes RHp=0 when the probability pi of thepast (or long-time) steering angle prediction error distribution 1 andthe probability qi of the current steering angle prediction errordistribution 2 are equal to each other, and the value of RHp increaseswhen the probabilities pi and qi are more different from each other.

Then, this process is terminated. The above-mentioned process isperformed repeatedly.

The range of the prediction error section bi for calculating the past(or long-time) steering angle prediction error distribution 1 and thecurrent steering angle prediction error distribution 2 may be set on thebasis of an α value used to calculate steering entropy Hp indicatingambiguity (uncertainty) of the steering error distribution. Here, the αvalue is calculated as a 90 percentile (a range of distributionincluding 90% of the steering error) by calculating the steering errorwithin a constant time, i.e., the difference between the estimatedsteering angle value and the actual steering angle when it is assumedthat the steering operation is smoothly carried out, on the basis oftime-series data of the steering angle and measuring a distribution(deviation) of the steering error.

Therefore, the α value is calculated on the basis of the past (orlong-time) steering angle prediction error distribution and the samerange of the prediction error sections bi is set for past (or long-time)steering angle prediction error distribution 1 and current steeringangle prediction error distribution 2 using the calculated α value. FIG.9 shows the ranges of the steering angle prediction error θe of thesections bi set using the α value.

(Operation and the Other)

When a learning situation can be considered as a learning-completedsituation (that the normal driving characteristics of a driver can beacquired) on the basis of the traveling time after collection of datastarts, the information provision device for use in a vehicle calculatesinstability using a steering entropy method through the use of processesof steps S1050 and S1060.

At this time, the information provision device for use in a vehiclecalculates the difference value between plural traveling statedistributions calculated and determines the unstable driving state onthe basis of the magnitude of the difference value. Accordingly, it ispossible to accurately detect an unstable traveling state regardless ofa variation in traffic environment. That is, it is possible toaccurately detect an unstable state depending on the normalcharacteristics of a driver regardless of a variation in trafficenvironment.

At this time, the information provision device for use in a vehiclecalculates plural traveling state distributions of different time rangesas plural traveling state distributions. For example, the informationprovision device for use in a vehicle calculates a traveling statedistribution including past traveling state data and a traveling statedistribution including immediately-previous traveling state data, anddirectly calculates the difference of the immediately-previous travelingstate distribution on the basis of the past traveling statedistribution. As a result, it is possible to evaluate stability of animmediately-previous state while continuously updating reference data.In this way, it is possible to accurately detect an unstable travelingstate regardless of a variation in traffic environment.

On the other hand, when it is determined that the learning situation isa learning-uncompleted situation (SD<1), the information provisiondevice for use in a vehicle compares the past traveling data with themagnitude of the single traveling state distribution which is thetraveling state distribution of the immediately-previous time rangeindicating the current traveling state and determines an unstabledriving state (steps S1070 to S1090). That is, by using a differentinstability calculating process at the time of incompletion of learning,it is possible to attract attention even when the traveling statedistribution of a driver is not known.

In the above-mentioned embodiment, the degree of learning SD iscalculated using the traveling time, it is estimated whether it can beestimated that the traveling state distribution is matched with thedriving characteristics of a driver on the basis of the degree oflearning SD, and it is thus determined whether the learning iscompleted. The degree of learning SD used to estimate that the travelingstate distribution is matched with the driving characteristics of adriver may be calculated based on a variation in the relative entropywhich is a feature amount of the traveling state distribution. Forexample, when the variation in the feature amount of the long-timetraveling state distribution is equal to or less than a predeterminedvalue, a value indicating the completion of learning is set as thedegree of learning SD.

Here, step S1010 constitutes the traveling state acquiring unit. StepS1050 constitutes the first traveling state distribution calculatingunit and the first driving instability determining unit. Step S1030constitutes the learning terminal determining unit. Steps S1070 andS1080 constitute the second driving instability determining unit. StepS1040 constitutes the instability selecting unit. Step S1100 constitutesthe information presenting unit.

(Advantages of this Embodiment)

(1) The traveling state acquiring unit acquires traveling state dataincluding at least one of a driving operation of a driver and a vehiclestate. The first traveling state distribution calculating unitcalculates plural traveling state distributions of different time rangeson the basis of the traveling state data acquired by the traveling stateacquiring unit. The first driving instability determining unit estimatesdriving instability on the basis of a difference value between pluraltraveling state distributions calculated by the first traveling statedistribution calculation unit. The learning completion determining unitdetermines that learning is completed when a predetermined learning timeelapses from the start of collection of the traveling state data, on thebasis of the degree of learning SD which is a degree at which thetraveling state distribution calculated by the first traveling statedistribution calculating unit is matched with the drivingcharacteristics of a driver. The degree of learning SD is calculated asa degree at which the traveling state distribution calculated by thefirst traveling state distribution calculating unit is matched with thedriving characteristics of a driver. The second driving instabilitydetermining unit estimates driving instability by comparing acomparative traveling state distribution acquired on the basis ofanother traveling state data different from the traveling state data ofthe immediately-previous time range, with the traveling statedistribution of the immediately-previous time range, which indicates thecurrent traveling state and calculated on the basis of the travelingstate data acquired by the traveling state acquiring unit. Theinstability selecting unit selects the instability estimated by thefirst driving instability determining unit when the learning iscompleted and selects the instability estimated by the second drivinginstability determining unit when the learning is completed, on thebasis of the determination result of the learning completion determiningunit. The information presenting unit presents instability informationbased on the instability selected by the instability selecting unit tothe driver.

By employing a different instability calculating unit at the time ofincompletion of learning, it is possible to attract attention even whenthe traveling state distribution of a driver is not known.

(2) The another traveling state data is traveling state data acquiredprior to the immediately-previous time range. The second drivinginstability determining unit compares the comparative traveling statedistribution with the traveling state distribution of theimmediately-previous time range by the use of a ratio of a featureamount of the traveling state distribution of the immediately-previoustime range and a reference feature amount, which is a value obtained bymultiplying a feature amount of the traveling state distributionacquired from the another traveling state data by a predeterminedcoefficient, and estimates the driving instability.

By calculating the reference feature amount by multiplying the featureamount of the traveling state distribution calculated from the differenttraveling data by the predetermined coefficient, it is possible toimprove the estimation accuracy of the driving instability through thecorrection using the coefficient even when the learning is notcompleted.

(3) The second driving instability determining unit estimates thedriving instability on the basis of at least one of information of thetraveling state data acquired by the traveling state acquiring unit anddriving scene information of the vehicle.

By using the vehicle behavior data, other traveling state data, and thedetermination result of a driving scene, it is possible to accuratelydetect a driver's driving state.

(4) The second driving instability determining unit estimates thedriving instability using one of the traveling state distributionscalculated on the basis of the traveling state data acquired by thetraveling state acquiring unit.

By using the traveling state distribution of a driver, it is possible toenable a statistical process and thus to improve accuracy.

(5) When the instability selecting unit selects the instabilityestimated by the second driving instability determining unit and theinstability estimated by the second driving instability determining unitis greater than a predetermined threshold value for determination, it isdetermined that the driving state is unstable.

Accordingly, it is possible to simply determine the unstable drivingstate.

(6) The threshold value for determination is set on the basis of data ofa past traveling history.

By using the past traveling history, it is possible to improve thedetection accuracy.

(7) The second driving instability determining unit includes a historystorage unit that stores the data of the past traveling history anddetermines the unstable state with reference to the tendency of the pasthistory stored in the history storage unit.

The determination using the past history improves the detectionperformance.

(8) The learning completion determining unit determines the degree oflearning SD using the traveling time.

By determining the learning situation using the traveling time, it ispossible to reduce erroneous determination of the completion oflearning.

(9) The learning completion determining unit determines the completionof learning using the variation in the feature amount of a singletraveling state distribution.

By determining the learning situation based on the variation in thefeature amount of the traveling state distribution, it is possible todetermine the completion of learning promptly.

(10) The traveling state distribution is calculated based on theoperation amount of a steering operation.

By detecting the traveling state distribution based on the steeringoperation requiring a continuous operation, it is possible to accuratelydetect the driving state.

(11) A steering entropy method is used to calculate the traveling statedistribution based on the operation amount of the steering operation.

By using the steering entropy method, it is possible to improve thedetection performance.

(12) The information presenting unit changes the instability informationto be presented depending on the learning completion result determinedby the learning completion determining unit.

By changing the information to be presented depending on thedetermination result of the completion of learning, it is possible toimprove the acceptability of a driver.

Second Embodiment

A second embodiment will be described below with reference to theaccompanying drawings. The same elements as in the first embodiment willbe referenced by the same reference signs.

The basic configuration of this embodiment is the same as in the firstembodiment. Both are different from each other, in the differentinstability calculating process performed when the learning situation isdetermined to be a learning uncompleted situation.

In this embodiment, the different instability calculating process isperformed by comparing the magnitude of a single traveling statedistribution with a value calculated based on distributions of generaldrivers and determining whether the driving state is unstable.

The process in an information providing unit 100A according to thisembodiment will be described below with reference to the flow chart ofFIG. 10.

Here, the processes of steps S2010 to S2070 are the same as theprocesses of steps S1010 to S1070 in the first embodiment. The processesof steps S2100 and S2110 are the same as the processes of steps S1100and S1110. Accordingly, such processes will not be described again.

The process of step S2080 will be described below.

In step S2080 in this embodiment, the information providing unit 100Areads feature amounts of general drivers stored in advance in a storageunit.

The feature amounts of general drivers are values obtained by performinga statistical process (for example, averaging) on the feature amountsacquired from plural drivers in advance. The feature amounts of thegeneral drivers may be appropriately updated through wirelesscommunications or the like. The feature amounts acquired from thedrivers are calculated from the traveling state data acquired from thedrivers.

The other processes are the same as in the first embodiment.

Here, step S2010 constitutes the traveling state acquiring unit. StepS2050 constitutes the first traveling state distribution calculatingunit and the first driving instability determining unit. Step S2030constitutes the learning terminal determining unit. Steps S2070 andS2080 constitute the second driving instability determining unit. StepS2040 constitutes the instability selecting unit. Step S2100 constitutesthe information presenting unit.

(Operational Advantages)

In this embodiment, the following advantages can be obtained in additionto the advantages described in the first embodiment.

(1) The another traveling state data used in the second drivinginstability determining unit is traveling state data acquired in advancefrom plural drivers. The threshold value for determination is acquiredfrom the characteristics of the traveling state distributions acquiredfrom the plural drivers.

By using the distributions serving as a reference of general drivers, itis possible to clarify the unstable state of a driver and thus toimprove the detection performance.

Third Embodiment

A third embodiment will be described below with reference to theaccompanying drawings. The same elements as in the first embodiment willbe referenced by the same reference signs.

The basic configuration of this embodiment is the same as in the firstembodiment. In the third embodiment, the different process ofcalculating a degree of instability when the learning situation isdetermined to be a learning-uncompleted situation is performed on thebasis of the history of the feature amount of a single distributionacquired based on the traveling state of the immediately-previous timerange.

The process in an information providing unit 100A according to thisembodiment will be described below with reference to the flow chart ofFIG. 11.

The processes of steps S3010 to S3070 are the same as the processes ofsteps S1010 to S1070 in the first embodiment. The process of steps S3100is the same as the process of steps S1100. Accordingly, such processeswill not be described again. Since it is not necessary to store thefeature amount in this embodiment, the process of step S1110 is notperformed.

The processes of steps S3080 and S3090 in this embodiment will bedescribed below.

In step S3080 of this embodiment, the information providing unit 100Astores several to ten pieces of instability calculated in step S3070 atconstant intervals and calculates a deviation, a variation, and anabsolute value so as to determine the tendency thereof.

The deviation is a deviation (standard deviation) of the pastinstability.

The variation is based on the comparison of the earliest instabilitywith the latest instability.

The absolute value is an absolute value of the latest instability.

In step S3090, the information providing unit 100A determines the pasthistory data calculated in step S3080.

For example, when the above-mentioned three items (deviation, variation,and absolute value) satisfy the conditions of a large deviation, a greatvariation, and a large absolute value, it is determined that the drivingstate is unstable. When same conditions of the three items satisfy theconditions, for example, when any one condition is satisfied, it may bedetermined that the driving state is unstable.

Here, when the deviation is larger than a predetermined threshold valuefor deviation, it is determined that the deviation is large. When theabsolute value of the variation is larger than a predetermined thresholdvalue for variation, it is determined that the variation is greater.When the absolute value is larger than a predetermined threshold valuefor absolute value, it is determined that the absolute value is large.

The other processes are the same as in the first embodiment.

Here, step S3010 constitutes the traveling state acquiring unit. StepS3050 constitutes the first traveling state distribution calculatingunit and the first driving instability determining unit. Step S3030constitutes the learning terminal determining unit. Steps S3070 andS3080 constitute the second driving instability determining unit. StepS3040 constitutes the instability selecting unit. Step S3100 constitutesthe information presenting unit.

(Operational Advantages)

As described above, in this embodiment, the feature amount (entropy) ofa single traveling state distribution is appropriately stored and theunstable driving is determined on the basis of the tendency (thevariation and the absolute value) thereof.

In this embodiment, the following advantages can be obtained in additionto the advantages described in the first embodiment.

(1) The second driving instability determining unit calculates thefeature amount of the traveling state distribution of theimmediately-previous time range indicating the current traveling statebased on the traveling state data acquired by the traveling stateacquiring unit, and estimates the degree of instability of the drivingon the basis of the calculated feature amount.

By using the feature amount of the traveling state distribution of adriver, it is possible to perform a statistical process and thus toimprove the accuracy.

(2) The second driving instability determining unit calculates thetendency of the feature amount on the basis of the history of thefeature amount acquired every constant interval, and estimates thedegree of driving instability on the basis of the calculated tendency.

According to this configuration, by using the tendency of the featureamount, it is possible to determine unstable driving without using thetraveling state distribution of a long-time time range.

Fourth Embodiment

A fourth embodiment will be described below with reference to theaccompanying drawings. The same elements as in the first embodiment willbe referenced by the same reference signs.

The basic configuration of this embodiment is the same as in the firstembodiment. In the fourth embodiment, the different process ofcalculating a degree of instability when the learning situation isdetermined to be a learning-uncompleted situation is performed on thebasis of the relative entropy values of different time ranges.

The process in an information providing unit 100A according to thisembodiment will be described below with reference to the flow chart ofFIG. 12.

The processes of steps S4010 to S4060 are the same as the processes ofsteps S1010 to S1060 in the first embodiment. The process of steps S4100is the same as the process of steps S1100. Accordingly, such processeswill not be described again. Since it is not necessary to store thefeature amount in this embodiment, the process of step S1110 is notperformed.

The processes of steps S4070 and S4090 will be described below.

In step S4030, the learning situation is determined as described above.

The learning situation is determined using the traveling time asdescribed above. The degree of learning SD is calculated, for example,using the following expression.

Degree of learning SD−traveling time (s)/(time range×coefficient)

Traveling time: time after travelingTime range: time range (for example, 2000 seconds) of a traveling statedistributionCoefficient: coefficient (for example, 5) associated with a convergencetime

In step S4070, the information providing unit 100A sets the time rangesin which two steering error distributions (traveling statedistributions) are calculated depending on the degree of learning SDcalculated in step S4030. In this embodiment, the two traveling statedistributions include a long traveling time distribution and a shorttraveling time distribution. The time range of the long traveling timedistribution is set to the time range based on the degree of learning SDas described below. The short traveling state distribution is calculatedas described above.

Time range of long-time traveling time distribution=degree of learningSD×learning coefficient

Degree of learning: value (0 to 1) calculated in step S4030Learning coefficient: learning time or a value obtained by multiplyingthe learning time by a predetermined coefficient (<1).

The learning coefficient is obtained, for example, by multiplying thelearning time (traveling time) by a predetermined constant of 1 or less.

In step S4080, the information providing unit 100A calculates a seconddistribution difference. The calculation is performed in the same waysas in step S4050 (step S1050). Here, the time range thereof is differentfrom that of the traveling state distribution for calculating a firstdistribution difference. The time range of the second traveling statedistribution is narrower than the time ranges of the first travelingstate distribution.

When the time ranges of the first traveling state distribution are setto Tw_s1 and Tw_l1 and the time ranges of the second first travelingstate distribution are set to Tw_s2 and Tw_l2, these values are set asfollows, for example.

Tw_s1: 60 (s) Tw_l1: 2000 (s) Tw_s2: 20 (s) Tw_l2: 600 (s)

In step S4080, the information providing unit 100A prepares twotraveling state distributions and calculates the differencetherebetween, on the basis thereon.

In step S4090, the information providing unit 100A determines theunstable driving state on the basis of the difference (feature amount)calculated in step S4080. In step S4090 of this embodiment, theinformation providing unit 100A compares the difference calculated instep S4080 with a predetermined threshold value for determination. Then,the information providing unit 100A determines that the driving state isunstable when the difference is larger than the threshold value fordetermination. Thereafter, the process progresses to step S4100.

Here, step S4010 constitutes the traveling state acquiring unit. StepS4050 constitutes the first traveling state distribution calculatingunit and the first driving instability determining unit. Step S4030constitutes the learning terminal determining unit. Steps S4070 to S4090constitute the second driving instability determining unit. Step S4070constitutes the second traveling state distribution calculating unit andthe second driving instability determining unit. Step S4040 constitutesthe instability selecting unit. Step S4100 constitutes the informationpresenting unit.

(Operational Advantages)

In this embodiment, when the learning situation is determined to be alearning-uncompleted situation, the information providing unit 100Acalculates two relative entropy values of different time ranges andchanges the time range of one relative entropy value depending on thelearning situation.

In this embodiment, the following advantages can be obtained in additionto the advantages of the first embodiment.

(1) The another traveling state data used in the second drivinginstability determining unit are traveling state data acquired inanother time range different from the immediately-previous time range.The another time range is changed depending on the degree of learning asa ratio of the time elapsing from the start of collection of thetraveling state data up to now to the learning time, and the anothertime range increases with an increase in the degree of learning.

By causing such another time range to increase as it gets closer to thecompletion of learning, it is possible to further smoothly change theinformation to be presented to a driver when the incompletion oflearning is transitioned to the completion of learning. For example, itis possible to further smoothly change the attraction of attention whenthe incompletion of learning is transitioned to the completion oflearning.

Fifth Embodiment

A fifth embodiment will be described below with reference to theaccompanying drawings. The same elements as in the first embodiment willbe referenced by the same reference signs.

The basic configuration of this embodiment is the same as in the firstembodiment. In the fifth embodiment, the different process ofcalculating a degree of instability when the learning situation isdetermined to be a learning-uncompleted situation is performed on thebasis of the feature amounts (relative entropy) of two singledistributions.

The system configuration of this embodiment is shown in FIG. 13. Asshown in FIG. 13, the outputs of the brake pedal operation amount sensorand an obstacle detecting device are input to the information providingunit 100A in addition to the outputs of the steering angle sensor 3 andthe timer 50.

The process of the information providing unit 100A according to thisembodiment will be described below with reference to the flowchart shownin FIG. 14.

First, in step S5010, the information providing unit 100A acquires thefollowing data as vehicle information data.

The information providing unit 100A acquires a steering angle and abrake pedal operation amount as operation information of a driver on thebasis of the outputs from the steering angle sensor 3 and the brakepedal operation amount sensor 2.

The information providing unit 100A acquires a vehicle velocity of thevehicle, a longitudinal G, a transverse G, a relative velocity to anobstacle ahead, and an inter-vehicle distance as information of avehicle state on the basis of the outputs of the vehicle velocity sensor4, the G sensor 8, and the vehicle ahead detecting device 9.

In step S5030, the information providing unit 100A determines a learningsituation through the same process as in step S1050.

In step S5040, the information providing unit 100A determines whetherthe learning situation is a learning completed situation on the basis ofthe degree of learning SD through the same process as in step S1040.When the learning situation is determined to be a learning-completedsituation, the process progresses to step S5050. On the other hand, whenthe learning situation is determined to be a learning-uncompletedsituation, the process progresses to steps S5080 and S5100.

In steps S5050 and S5060, the same processes as in steps S1050 and S1060are performed. That is, in step S5050, the difference value between thedistributions is calculated. In step S5060, the calculated differencevalue is compared with a predetermined threshold value for determinationand it is determined whether the driving state is unstable.

In step S5070, the information providing unit 100A performs aninformation presenting process when it is determined in step S5060 thatthe driving state is unstable.

On the other hand, in step S5080, the information providing unit 100Acalculates a steering entropy value using the steering angle. In stepS5080, the absolute entropy value (feature amount) as an instabilityvalue is calculated based on a single traveling state distribution.Thereafter, the process progresses to step S5090.

In step S5090, the information providing unit 100A performs the sameprocess as in step S2080 and reads the feature amounts of generaldrivers stored in the storage unit. Thereafter, the process progressesto step S5120.

In step S5100, the information providing unit 100A calculates theabsolute entropy value (feature amount) as an instability value based onthe magnitude of the TTC (Time To Collision) at the time of braking.Thereafter, the process progresses to step S5110. Here, the time tocollision (TTC) represents the time until colliding with an obstaclewhen the vehicle travels while maintaining the traveling state at thetime of calculating the time to collision.

When the state of a driver is unstable, it is generally known that thebraking timing is delayed. Therefore, it is possible to determine anunstable state by evaluating the braking timing. The braking timing isused after being normalized by using statistical data of a generalbraking operation.

For example, when the number of braking operations is N, it is assumedthat TTC at the time of braking are defined as TTC1, TTC2, . . . . Whenthe average value of general braking timing is defined as μ and thestandard deviation thereof is defined as σ, the normalized values Std ofthe braking operations can be expressed as follows.

$\begin{matrix}{{{Std}\; 1} = {\left( {\mu \text{-}{TTC}\; 1} \right)/\sigma}} \\{{{Std}\; 2} = {\left( {\mu \text{-}{TTC}\; 2} \right)/\sigma}} \\{{{Std}\; 3} = {\left( {\mu \text{-}{TTC}\; 3} \right)/\sigma}} \\\ldots \\{{Stdn} = {\left( {\mu \text{-}{TTCn}} \right)/\sigma}}\end{matrix}\quad$

The average Std (ΣStdn (where n is a value of 1 to n)/N) is used as adegree of instability.

In step S5110, the information providing unit 100A reads an averagevalue of TTC (a value set in advance in the storage unit) which isgenerally allowable. This value is set to, for example, a value between2 and 3.

In step S5120, the information providing unit 100A determines that thedriving state is unstable when any one of the first single distributioninstability using the feature amount based on the processes of stepS5080 and S5090 and the second single distribution instability using thefeature amount based on the processes of steps S5100 and S5110 satisfiesthe following conditional expressions. When the following conditionalexpressions are satisfied, it is determined that the driving state isunstable.

Degree of instability calculated in step S5080>general feature amountread in step S5090

Degree of instability calculated in step S5100>general feature amountread in step S5110

In steps S5080 and S5100 of this embodiment, cases where the degree ofinstability is calculated based on the steering entropy and the TTC atthe time of braking are exemplified, respectively. Instead, any of thedriving operation of a driver and a vehicle behavior index (in addition,a frequency distribution of a transverse G and a longitudinal G) due tothe driving operation may be used.

In step S5130, when it is determined in step S5120 that the drivingstate is unstable, the information providing unit 100A performs aninformation presenting process.

The other configurations are the same as in the first embodiment.

Here, step S5010 constitutes the traveling state acquiring unit. StepS5050 constitutes the first traveling state distribution calculatingunit and the first driving instability determining unit. Step S5030constitutes the learning terminal determining unit. Steps S5080 andS5120 constitute the second driving instability determining unit. StepS5040 constitutes the instability selecting unit. Steps S5070 and S5130constitute the information presenting unit.

(Operational Advantages)

In this embodiment, the degree of instability is determined inconsideration of other indices (such as the transverse G in right orleft turn) in addition to the steering entropy. That is, two types ofsingle traveling state distributions (which are calculated fromdifferent indices, respectively) are used and it is determined that thedriving state is unstable when it is determined that any one thereof isunstable.

In this embodiment, the following advantages can be obtained in additionto the advantages described in the first embodiment.

(1) The another traveling state data used in the second drivinginstability determining unit is plural types of traveling state data.The second driving instability determining unit estimates each of pluraltraveling state distributions based on the plural types of travelingstate data as comparative traveling state distributions respectively.That is, the second driving instability determining unit estimates thedegree of driving instability from the feature amounts of pluraltraveling state distributions acquired from the plural types oftraveling state data.

By detecting the state of a driver from plural signals as well asspecific signals, it is possible to improve the detection performance.

(2) The second driving instability determining unit estimates the degreeof driving instability using the time to collision (TTC).

By using the time to collision, it is possible to accurately detect adecelerating operation state.

Sixth Embodiment

A sixth embodiment will be described below with reference to theaccompanying drawings. The same elements as in the first embodiment willbe referenced by the same reference signs.

The basic configuration of this embodiment is the same as in the firstembodiment. In the sixth embodiment, the different process ofcalculating a degree of instability when the learning situation isdetermined to be a learning-uncompleted situation is performed bycalculating the feature amount of a specific driving scene.

FIG. 15 is a diagram illustrating a system configuration according tothis embodiment. In this embodiment, intersection information isacquired through the use of a blinker indicating signal or a navigationsystem so as to detect a specific driving scene.

The process of the information providing unit 100A according to thisembodiment will be described below with reference to the flowchart shownin FIG. 16.

In step S6010, the following data is acquired as vehicle informationdata which is information on the driving operation of a driver and avehicle state due to the driving operation.

A steering angle, an accelerator pedal opening degree, a brake pedaloperation amount, and a blinker indicating signal are acquired as theinformation on the driving operation of a driver. The blinker indicatingsignal is used as information for detecting a driving scene.

A vehicle velocity, a longitudinal G, and a transverse G are detected asthe vehicle state information.

In step S6030, the information providing unit 100A acquires theintersection information as the traffic environment information throughthe navigation system 7.

In step S6030, the information providing unit 100A determines a learningsituation through the same process as step S1030.

In step S6040, the information providing unit 100A determines whetherthe learning situation is a learning-completed situation on the basis ofthe degree of learning SD through the same process as in step S1040.When the learning situation is determined to be a learning-completedsituation, the process progresses to step S6050. On the other hand, whenthe learning situation is determined to be a learning-uncompletedsituation, the process progresses to step S6070.

In step S6050, the information providing unit 100A calculates thedifference value between the distributions through the same process asstep S1050. In step S6060, the unstable driving state is determined onthe basis of the difference value through the same process as stepS1060. Thereafter, the process progresses to step S6100.

On the other hand, in step S6070, the information providing unit 100Adetermines a driving scene (traffic environment). A right or leftturning scene at an intersection, a preceding vehicle approaching scene,and the like can be considered as the driving scene, and the right orleft turning scene at an intersection is exemplified.

It can be determined whether the driving scene is the right or leftturning scene at an intersection, by using a method of determining anintersection on a navigation map or a process of determining anintersection on the basis of the blinker or the vehicle behavior.

In step S6080, the information providing unit 100A calculates thefeature amount. A method same as the process in step S5100 of the fifthembodiment is used to calculate the feature amount in step S6080. Thatis, the feature amount is calculated by applying the magnitude of thetransverse G instead of the braking operation to the process of stepS5100.

In step S6090, the information providing unit 100A compares the featureamount calculated in step S6080 with a predetermined threshold value fordetermination and determines that the driving state is unstable when thefeature amount is larger than the threshold value for determination.

The threshold value for determination is changed depending on thedetected driving scene. For example, when the detected driving scene isa right or left turning scene at an intersection, the threshold valuefor determination is set to be lower than those of the other drivingscenes.

In step S6100, similarly to step S1100, the information presentingprocess is performed when it is determined in step S6060 or S6090 thatthe driving state is unstable.

Here, the right or left turning scene at an intersection is exemplifiedas the driving scene, but a preceding vehicle approaching scene or thelike may be used. In this case, in step S6070, it is determined whetherthe vehicle approaches a preceding vehicle. For example, when thedistance to the preceding vehicle is equal to or less than apredetermined distance, it is determined that the driving scene is thepreceding vehicle approaching scene. In step S6080, the same process asstep S5100 is performed. In step S6090, the threshold value fordetermination is set to be lower than those of the other driving sceneswhen it is determined that the driving scene is the preceding vehicleapproaching scene.

Here, steps S6010 and S6020 constitute the traveling state acquiringunit. Step S6050 constitutes the first traveling state distributioncalculating unit and the first driving instability determining unit.Step S6030 constitutes the learning terminal determining unit. StepsS6070 to S6090 constitute the second driving instability determiningunit. Step S6040 constitutes the instability selecting unit. Step S6100constitutes the information presenting unit.

(Operational Advantages)

In this embodiment, the driving scene is determined, and then, a featureamount in a specific driving scene is calculated. The unstable state isdetermined on the basis of the feature amount in the specific drivingscene (such as the right or left turning scene at an intersection or thedecelerating scene relative to a preceding vehicle).

In this embodiment, the following advantages can be obtained in additionto the advantages of the first embodiment.

(1) The second driving instability determining unit includes a drivingscene detecting unit that detects a specific driving scene and estimatesa degree of instability by calculating a feature amount on the basis ofthe driving operation data of the specific driving scene detected by thedriving scene detecting unit.

By specifying the driving scene, it is possible to easily grasp a smallvariation in driving operation appearing in the unstable state.

(2) The right or left turning scene at an intersection is detected asthe specific driving scene, the feature amount is calculated from thedriving characteristic at that time, and the degree of instability isestimated.

It is possible to accurately detect a state of a driver using thedriving characteristics of the right or left turning scene at anintersection.

(3) The driving characteristics in the right or left turning sceneemploy the magnitude of the transverse G.

It is possible to accurately detect a behavior state at an intersectionusing the transverse G.

(4) The preceding vehicle approaching scene is detected as the specificdriving scene, the feature amount is calculated from the drivingcharacteristic of the deceleration operation at that time, and thedegree of instability is estimated.

By detecting the decelerating operation characteristic of the precedingvehicle approaching scene, it is possible to accurately detect a stateof a driver.

(5) The driving characteristics of the decelerating operation employ themagnitude of the TTC (time to collision) at the time of braking.

By using the time to collision, it is possible to accurately detect astate of a driver in the preceding vehicle approaching scene.

Priority is claimed on Japanese Patent Application No. 2011-94343 (filedon Apr. 20, 2011), the content of which is incorporated herein byreference in entirety.

While the present invention has been described with reference to thedefinite number of embodiments, the scope of the present invention isnot limited thereto and improvements and modifications of theembodiments based on the above disclosure are obvious to those skilledin the art.

REFERENCE SIGNS LIST

-   -   SD: degree of learning    -   TTC: time to collision    -   1: accelerator pedal opening degree sensor    -   2: brake pedal operation amount sensor    -   3: steering angle sensor    -   4: vehicle velocity sensor    -   5: blinker detecting sensor    -   6: meter display    -   7: navigation system    -   8: G sensor    -   9: vehicle ahead detecting device    -   10: speaker    -   50: timer    -   100: controller    -   100A: information providing unit

1. An information provision device for use in a vehicle, comprising: atraveling state acquiring unit for acquiring traveling state dataincluding at least one of a driving operation of a driver and a vehiclestate; a first traveling state distribution calculating unit forcalculating a plurality of traveling state distributions including atraveling state distribution of an immediately-previous time rangeindicating a current traveling state and a traveling state distributionof a time range different from the immediately-previous time range onthe basis of the traveling state data acquired by the traveling stateacquiring unit; a first driving instability determining unit forestimating first driving instability on the basis of a difference valuebetween the plurality of traveling state distributions calculated by thefirst traveling state distribution calculation unit; a learningcompletion determining unit for determining that learning is completedwhen a predetermined learning time elapses from start of collection ofthe traveling state data; a driving scene detecting unit for detectingwhether a driving scene is a specific driving scene set in advance foruse when the learning is not completed, when it is determined that thelearning is not completed on the basis of the determination result ofthe learning completion determining unit; a second driving instabilitydetermining unit for calculating, when it is determined that thelearning is not completed on the basis of a determination result of thelearning completion determining unit and the driving scene detectingunit detects the specific driving scene, a feature amount of thetraveling state distribution of the immediately-previous time rangewhich indicates a current traveling state based on the traveling statedata acquired by the traveling state acquiring unit in the specificdriving scene detected by the driving scene detecting unit andestimating second driving instability on the basis of the calculatedfeature amount; an instability selecting unit for selecting the firstdriving instability when the learning is completed and selecting thesecond driving instability when the learning is not completed, on thebasis of the determination result of the learning completion determiningunit; and an information presenting unit for presenting instabilityinformation based on instability selected by the instability selectingunit between the first driving instability and the second drivinginstability to the driver.
 2. The information provision device for usein the vehicle according to claim 1, wherein the second drivinginstability determining unit calculates a tendency of the feature amounton the basis of a history of the feature amount acquired at a constantinterval and estimates the second driving instability on the basis ofthe calculated tendency.
 3. The information provision device for use inthe vehicle according to claim 1, wherein a right or left turn at anintersection is detected as the specific driving scene and the featureamount is calculated from a driving characteristic at that time.
 4. Theinformation provision device for use in the vehicle according to claim3, wherein the magnitude of a transverse G is used as the drivingcharacteristic of the right or left turn.
 5. The information provisiondevice for use in the vehicle according to claim 1, wherein a precedingvehicle approaching scene is detected as the specific driving scene andthe feature amount is calculated from the driving characteristic of adecelerating operation at that time.
 6. The information provision devicefor use in the vehicle according to claim 5, wherein the magnitude of atime to collision (TTC) at the time of braking is used as the drivingcharacteristic of the decelerating operation.
 7. The informationprovision device for use in the vehicle according to claim 1, whereinthe second driving instability determining unit determines that thedriving state is unstable when the calculated feature amount is greaterthan a predetermined threshold value for determination.
 8. Theinformation provision device for use in the vehicle according to claim1, wherein the traveling state distribution is calculated based on anoperation amount of steering operation.
 9. The information provisiondevice for use in the vehicle according to claim 8, wherein a steeringentropy method is used in calculation based on the operation amount ofsteering operation.
 10. The information provision device for use in thevehicle according to claim 1, wherein the second driving instabilitydetermining unit estimates the second driving instability using a timeto collision (TTC).
 11. The information provision device for use in thevehicle according to claim 1, wherein the information presenting unitchanges the instability information to be presented depending on whetheror not the learning is completed.